Applying interpretable machine learning in computational biology-pitfalls, recommendations and opportunities for new...

Area of Science:

• Computational Biology
• Machine Learning
• Bioinformatics

Background:

• Machine learning advances enable predictive models for complex biological problems.
• Interpretable machine learning (IML) is crucial for uncovering biological insights from these models.
• Current guidelines for applying IML in computational biology are insufficient.

Purpose of the Study:

• To provide an overview of interpretable machine learning methods and evaluation techniques.
• To discuss common challenges and pitfalls when applying IML to computational biology.
• To identify open questions and encourage interdisciplinary collaboration.

Main Methods:

• Literature review of interpretable machine learning techniques.
• Analysis of common pitfalls in computational biology applications.
• Discussion of future research directions, including large language models.

Main Results:

• Identified a need for standardized guidelines in IML for computational biology.
• Detailed common challenges in applying IML methods to biological data.
• Highlighted the potential of IML in advancing biological discovery.

Conclusions:

• There is a critical need for robust guidelines and best practices for IML in computational biology.
• Addressing current pitfalls will enhance the reliability and interpretability of machine learning models in biology.
• Fostering collaboration between IML and computational biology researchers is essential for future progress.